Description
Course info
Level
Beginner
Updated
Jun 22, 2018
Duration
1h 44m
Description

As machine learning and deep learning techniques become popular, getting the dataset into the right numeric form and engineering the right features to feed into ML models becomes critical. In this course, Working with Multidimensional Data Using NumPy, you'll learn the simple and intuitive functions and classes that NumPy offers to work with data of high dimensionality. First, you will get familiar with basic operations to explore multi-dimensional data, such as creating, printing, and performing basic mathematical operations with arrays. You'll study indexing and slicing of array data and iterating over lists and see how images are basically 3D arrays and how they can be manipulated with NumPy. Next, you will move on to complex indexing functions. NumPy arrays can be indexed with conditional functions as well as arrays of indices. You'll then see how broadcasting rules work which allows NumPy to perform operations on arrays with different shapes as well as, study array operations such as np.argmax() which are very common when working with ML problems. Finally, you'll study how NumPy integrates with other libraries in the PyData stack. You will also cover specific implementations with SciPy and with Pandas. At the end of this course, you will be comfortable using the array manipulation techniques that NumPy has to offer to get your data in the right form for extracting insights.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi and welcome to this course on Working with Multidimensional Data Using NumPy. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real-time collaborative editing in Google Docs and I hold four patents for its underlying technologies. I currently working on my own startup, Loonycorn, a studio for high-quality video content. In this course we learn the simple and intuitive functions and classes that NumPy offers to work with data of high dimensionality. We start off with basic operations to explore multi-dimensional data such as creating, printing, and performing basic mathematical operations on arrays. We study indexing and slicing of array data and iterating over these lists. We'll see how images are basically three-dimensional arrays and how they can be manipulated with NumPy. We then move on to complex indexing functions. NumPy arrays can be indexed with conditional functions as the less arrays of indices. We'll then see how broadcasting rules works. This allows NumPy to perform operations on arrays with different shapes. We'll study array operations such as the np. argmax, which are very commonly used when working with ML problems. Then we'll move on to studying how NumPy integrates with other libraries in the PyData stack. We cover specific implementations with SciPy as well as with Pandas. At the end of this course you will be very comfortable using the array manipulation techniques that NumPy has to offer to get your data in the right form for extracting in sites.